Highlights Quality control-driven framework for cardiac segmentation and quality control. Exploiting variability within deep neural network ensemble to estimate uncertainty. Novel on-the-fly selection mechanism for the final optimal segmentation. Accurate, reliable, and fully automated analysis of T1 map with visualization. Highlighting a potential flaw of the Pearson correlation to evaluate quality score.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterization, but requires intravenous contrast agent administration. It is highly desired to develop a contrast-agent-free technology to replace LGE for faster and cheaper CMR scans. Methods: A CMR Virtual Native Enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1-maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multi-center Hypertrophic Cardiomyopathy Registry (HCMR), using HCM as an exemplar. The datasets were randomized into two independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement and myocardial lesion burden quantification. Image quality was compared using nonparametric Wilcoxon test. Intra- and inter-observer agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. Results: 1348 HCM patients provided 4093 triplets of matched T1-maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development, and 345 for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets, p<0.001, Wilcoxon test). VNE revealed characteristic HCM lesions in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyper-intensity myocardial lesions (r=0.77-0.79, ICC=0.77-0.87; p<0.001) and intermediate-intensity lesions (r=0.70-0.76, ICC=0.82-0.85; p<0.001). The native CMR images (cine plus T1-map) required for VNE can be acquired within 15 minutes. Producing a VNE image takes less than one second. Conclusions: VNE is a new CMR technology that resembles conventional LGE, without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Platelet activation plays a pivotal role in acute coronary syndrome (ACS). We investigated the relationship between platelet distribution width (PDW) and severity of coronary artery disease (CAD) in patients with ACS. A total of 502 patients with ACS were enrolled. High (n = 151) and low PDW (n = 351) groups were defined as patients having values in the third tertile (>17%) and lower 2 tertiles (≤17%). There were significantly higher Gensini score (44 [10-168] vs. 36 [2-132], P < .001), and neutrophil-lymphocyte ratio (3.1 [0.8-12.4] vs. .2.5 [0.3-13], P = .012) and baseline platelet counts were significantly lower (220 [61-623] vs. 233 [79-644] 10(3)/mm3, P = .022) in the high PDW group. The variables PDW >17%, diabetes mellitus, and myocardial infarction were found to be associated with high Gensini score (odds ratio [OR]: 1.91, 95% confidence interval [CI]: 1.27-2.88, P = .002; OR: 2.85, 95% CI: 1.91-4.25, P < .001; OR: 2.67, 95% CI:1.74-4.1, P < .001, respectively). An increased PDW (>17%) is associated with severity of CAD in patients with ACS.

The predictive value of leukocyte counts has been reported in patients with acute myocardial infarction (AMI). We aimed to evaluate the predictive value of the total leukocyte count and neutrophil-lymphocyte (N/L) ratio for mortality due to AMI. A total of 522 patients with acute ST-elevated MI were included in the study. The study population was divided into tertiles based on admission N/L ratio values. High (n = 174) and low N/L (n = 348) ratio groups were defined as patients having values in the third tertile (>5.77) and lower 2 tertiles (≤ 5.77), respectively. The high N/L ratio group had a significantly higher incidence of in-hospital cardiovascular mortality (13.8% vs 4.6%, P < .001). An N/L ratio >5.77 was found to be an independent predictor of in-hospital cardiovascular mortality (hazard ratio: 3.78, 95% confidence interval: 1.71-8.30, P = .001). A high N/L ratio is a strong and independent predictor of in-hospital cardiovascular mortality of AMI with ST elevation.

Hs-TnT and NLR were significantly correlated with angiographic severity of ACS assessed by SYNTAX Score.

Background: Diabetes mellitus (DM) is associated with increased risk of cardiovascular disease. Detection of early cardiac changes before manifest disease develops is important. We investigated early alterations in cardiac structure and function associated with DM using cardiovascular magnetic resonance imaging. Methods: Participants from the UK Biobank Cardiovascular Magnetic Resonance Substudy, a community cohort study, without known cardiovascular disease and left ventricular ejection fraction ≥50% were included. Multivariable linear regression models were performed. The investigators were blinded to DM status. Results: A total of 3984 individuals, 45% men, (mean [SD]) age 61.3 (7.5) years, hereof 143 individuals (3.6%) with DM. There was no difference in left ventricular (LV) ejection fraction (DM versus no DM; coefficient [95% CI]: −0.86% [−1.8 to 0.5]; P =0.065), LV mass (−0.13 g/m 2 [−1.6 to 1.3], P =0.86), or right ventricular ejection fraction (−0.23% [−1.2 to 0.8], P =0.65). However, both LV and right ventricular volumes were significantly smaller in DM, (LV end-diastolic volume/m 2 : −3.46 mL/m 2 [−5.8 to −1.2], P =0.003, right ventricular end-diastolic volume/m 2 : −4.2 mL/m 2 [−6.8 to −1.7], P =0.001, LV stroke volume/m 2 : −3.0 mL/m 2 [−4.5 to −1.5], P <0.001; right ventricular stroke volume/m 2 : −3.8 mL/m 2 [−6.5 to −1.1], P =0.005), LV mass/volume: 0.026 (0.01 to 0.04) g/mL, P =0.006. Both left atrial and right atrial emptying fraction were lower in DM (right atrial emptying fraction: −6.2% [−10.2 to −2.1], P =0.003; left atrial emptying fraction:−3.5% [−6.9 to −0.1], P =0.043). LV global circumferential strain was impaired in DM (coefficient [95% CI]: 0.38% [0.01 to 0.7], P =0.045). Conclusions: In a low-risk general population without known cardiovascular disease and with preserved LV ejection fraction, DM is associated with early changes in all 4 cardiac chambers. These findings suggest that diabetic cardiomyopathy is not a regional condition of the LV but affects the heart globally.

BackgroundPlatelet-to-lymphocyte ratio (PLR) is a new prognostic marker in coronary artery disease. We aimed to evaluate the relationship between PLR and in-hospital mortality in patients with ST-elevated acute myocardial infarction (AMI).Material/MethodsThe present study included 636 patients with ST-elevated AMI. The study population was divided into tertiles based on their admission PLR. Patients having values in the third tertile was defined as the high PLR group (n=212) and those having values in the lower 2 tertiles were defined as the low PLR group (n=424).ResultsRisk factors of coronary artery disease and treatments administered during the in-hospital period were similar between the groups. Male patient ratio was found to be lower in the high PLR group (73% vs. 82.8%, p=0.004). In-hospital mortality was increased in the high PLR group when compared to the low PLR group (12.7% vs. 5.9%, p=0.004). The PLR >144 was found to be an independent predictor of in-hospital cardiovascular mortality (HR: 2.16, 95% CI: 1.16–4.0, p=0.014).ConclusionsThis study showed that PLR is an independent predictor of cardiovascular mortality in patients with ST-elevated AMI.

C ardiovascular MRI tissue tagging is the noninvasive reference standard for myocardial strain estimation (1-4). Although cardiovascular MRI feature tracking allows calculation of strain from standard steady-state free precession images, features are limited to myocardial edges (5) and structures outside the myocardium (6), whereas cardiovascular MRI tagging enables detection and tracking of features within the myocardium. Displacement encoding with stimulated echoes (7,8) has the potential to provide higher spatial resolution strain estimates (9) but, to date, has not been as widely used (10). The utility of cardiovascular MRI tagging has been demonstrated in many different patient groups (4). However, there is a lack of robust fully automated analysis tools for the quantification of strain from cardiovascular MRI-tagged images, leading to analysis times that are prohibitive in a high-throughput setting, such as studies with many hundreds of cases or high-volume clinical centers with more than 20 cases per week (4,11,12). The most common approaches for strain analysis of cardiovascular MRI-tagged images include profile matching and spline fitting (13), deformable contours (14), harmonic phase analysis (15), and sine wave modeling (16). However, these methods require manual initialization and lack robustness. Recently, deep learning methods, particularly convolutional neural networks